
#from IPython.core.display import HTML


# HTML('''<div class="flourish-embed flourish-cards" data-src="visualisation/1810417" data-url="https://flo.uri.sh/visualisation/1810417/embed"><script src="https://public.flourish.studio/resources/embed.js"></script></div>''')


#HTML('''<div class="flourish-embed flourish-cards" data-src="visualisation/1816605" data-url="https://flo.uri.sh/visualisation/1816605/embed"><script src="https://public.flourish.studio/resources/embed.js"></script></div>''')


# #get latest version of pip
# !pip install --upgrade pip

# #for calender map
# ! pip install calmap

# #to convert us statenames to state codes
# # ! pip install us

# #to get continent name from country name
# ! pip install pycountry_convert


# math opeations
import math
# produce random numbers
import random
# to load json files
import json
# datetime oprations
from datetime import timedelta
#  to get web contents
from urllib.request import urlopen

# for numerical analyiss
import numpy as np
# to store and process data in dataframe
import pandas as pd


# basic visualization package
import matplotlib.pyplot as plt
# advanced ploting
import seaborn as sns

# interactive visualization
import plotly.express as px
import plotly.graph_objs as go
# import plotly.figure_factory as ff
from plotly.subplots import make_subplots


# for offline ploting
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)


# converter
# from pandas.plotting import register_matplotlib_converters
# register_matplotlib_converters()   

# hide warnings
import warnings
warnings.filterwarnings('ignore')

# to USA states details
# import us


# color pallette
cnf, dth, rec, act = '#393e46', '#ff2e63', '#21bf73', '#fe9801' 

# seaborn plot style
# sns.set_style('darkgrid')


# list files
# ==========

# !ls ../input/corona-virus-report


# Full data
# =========

full_table = pd.read_csv('./Covid 19 data/covid_19_clean_complete.csv')
# full_table.head()


# Grouped by day, country
# =======================

full_grouped = pd.read_csv('./Covid 19 data/full_grouped.csv')
full_grouped['Date'] = pd.to_datetime(full_grouped['Date'])
# full_grouped.head()


# Day wise
# ========

day_wise = pd.read_csv('./Covid 19 data/day_wise.csv')
day_wise['Date'] = pd.to_datetime(day_wise['Date'])
# day_wise.head()


# Country wise
# ============

country_wise = pd.read_csv('./Covid 19 data/country_wise_latest.csv')
country_wise = country_wise.replace('', np.nan).fillna(0)
# country_wise.head()


# Worldometer data
# ================

worldometer_data = pd.read_csv('./Covid 19 data/worldometer_data.csv')
worldometer_data = worldometer_data.replace('', np.nan).fillna(0)
# worldometer_data.head()


temp = day_wise[['Date','Deaths', 'Recovered', 'Active']].tail(1)
temp = temp.melt(id_vars="Date", value_vars=['Active', 'Deaths', 'Recovered'])
fig = px.treemap(temp, path=["variable"], values="value", height=225, 
                 color_discrete_sequence=[act, rec, dth])
fig.data[0].textinfo = 'label+text+value'
fig.show()


def plot_map(df, col, pal):
    df = df[df[col]>0]
    fig = px.choropleth(df, locations="Country/Region", locationmode='country names', 
                  color=col, hover_name="Country/Region", 
                  title=col, hover_data=[col], color_continuous_scale=pal)
#     fig.update_layout(coloraxis_showscale=False)
    fig.show()


plot_map(country_wise, 'Confirmed', 'matter')


plot_map(country_wise, 'Deaths', 'matter')


plot_map(country_wise, 'Deaths / 100 Cases', 'matter')


# Over the time

fig = px.choropleth(full_grouped, locations="Country/Region", 
                    color=np.log(full_grouped["Confirmed"]),
                    locationmode='country names', hover_name="Country/Region", 
                    animation_frame=full_grouped["Date"].dt.strftime('%Y-%m-%d'),
                    title='Cases over time', color_continuous_scale=px.colors.sequential.matter)
fig.update(layout_coloraxis_showscale=False)
fig.show()


def plot_daywise(col, hue):
    fig = px.bar(day_wise, x="Date", y=col, width=700, color_discrete_sequence=[hue])
    fig.update_layout(title=col, xaxis_title="", yaxis_title="")
    fig.show()


def plot_daywise_line(col, hue):
    fig = px.line(day_wise, x="Date", y=col, width=700, color_discrete_sequence=[hue])
    fig.update_layout(title=col, xaxis_title="", yaxis_title="")
    fig.show()


temp = full_grouped.groupby('Date')['Recovered', 'Deaths', 'Active'].sum().reset_index()
temp = temp.melt(id_vars="Date", value_vars=['Recovered', 'Deaths', 'Active'],
                 var_name='Case', value_name='Count')
temp.head()

fig = px.area(temp, x="Date", y="Count", color='Case', height=600, width=700,
             title='Cases over time', color_discrete_sequence = [rec, dth, act])
fig.update_layout(xaxis_rangeslider_visible=True)
fig.show()


plot_daywise('Confirmed', '#333333')


plot_daywise('Active', '#333333')


plot_daywise('New cases', '#333333')


plot_daywise('Deaths', dth)


plot_daywise('New deaths', dth)


plot_daywise('Recovered', rec)


plot_daywise('New recovered', rec)


plot_daywise_line('Deaths / 100 Cases', dth)


plot_daywise_line('Deaths / 100 Recovered', dth)


plot_daywise_line('Recovered / 100 Cases', rec)


plot_daywise('No. of countries', '#035aa6')


temp = day_wise[['Date', 'Recovered', 'Active']]
temp = temp.melt(id_vars='Date', value_vars=['Recovered', 'Active'], 
                 var_name='Variable', value_name='Count')
px.line(temp, x='Date', y='Count', color='Variable')


def plot_hbar(df, col, n, hover_data=[]):
    fig = px.bar(df.sort_values(col).tail(n), 
                 x=col, y="Country/Region", color='WHO Region',  
                 text=col, orientation='h', width=700, hover_data=hover_data,
                 color_discrete_sequence = px.colors.qualitative.Dark2)
    fig.update_layout(title=col, xaxis_title="", yaxis_title="", 
                      yaxis_categoryorder = 'total ascending',
                      uniformtext_minsize=8, uniformtext_mode='hide')
    fig.show()


def plot_hbar_wm(col, n, min_pop=1000000, sort='descending'):
    df = worldometer_data[worldometer_data['Population']>min_pop]
    df = df.sort_values(col, ascending=True).tail(n)
    fig = px.bar(df,
                 x=col, y="Country/Region", color='WHO Region',  
                 text=col, orientation='h', width=700, 
                 color_discrete_sequence = px.colors.qualitative.Dark2)
    fig.update_layout(title=col+' (Only countries with > 1M Pop)', 
                      xaxis_title="", yaxis_title="", 
                      yaxis_categoryorder = 'total ascending',
                      uniformtext_minsize=8, uniformtext_mode='hide')
    fig.show()


plot_hbar(country_wise, 'Confirmed', 15)


plot_hbar(country_wise, 'Active', 15)


plot_hbar(country_wise, 'New cases', 15)


plot_hbar(country_wise, 'Deaths', 15)


plot_hbar(country_wise, 'New deaths', 15)


plot_hbar(country_wise, 'Deaths / 100 Cases', 15)


plot_hbar(country_wise, 'Recovered', 15)


plot_hbar(country_wise, 'New recovered', 15)


plot_hbar(country_wise, 'Recovered / 100 Cases', 15)


plot_hbar(country_wise, '1 week change', 15)


plot_hbar(country_wise, '1 week % increase', 15)


plot_hbar_wm('Tot Cases/1M pop', 15, 1000000)


plot_hbar_wm('Deaths/1M pop', 15, 1000000)


plot_hbar_wm('TotalTests', 15, 1000000)


plot_hbar_wm('Tests/1M pop', 15)


def plot_stacked(col):
    fig = px.bar(full_grouped, x="Date", y=col, color='Country/Region', 
                 height=600, title=col, 
                 color_discrete_sequence = px.colors.cyclical.mygbm)
    fig.update_layout(showlegend=True)
    fig.show()


def plot_line(col):
    fig = px.line(full_grouped, x="Date", y=col, color='Country/Region', 
                  height=600, title=col, 
                  color_discrete_sequence = px.colors.cyclical.mygbm)
    fig.update_layout(showlegend=True)
    fig.show()


plot_stacked('Confirmed')


plot_stacked('Deaths')


plot_stacked('New cases')


plot_stacked('Active')


plot_line('Confirmed')


plot_line('Deaths')


plot_line('New cases')


plot_line('Active')


temp = pd.merge(full_grouped[['Date', 'Country/Region', 'Confirmed', 'Deaths']], 
                day_wise[['Date', 'Confirmed', 'Deaths']], on='Date')
temp['% Confirmed'] = round(temp['Confirmed_x']/temp['Confirmed_y'], 3)*100
temp['% Deaths'] = round(temp['Deaths_x']/temp['Deaths_y'], 3)*100
temp.head()


fig = px.bar(temp, x='Date', y='% Confirmed', color='Country/Region', 
             range_y=(0, 100), title='% of Cases from each country', 
             color_discrete_sequence=px.colors.qualitative.Prism)
fig.show()


fig = px.bar(temp, x='Date', y='% Deaths', color='Country/Region', 
             range_y=(0, 100), title='% of Cases from each country', 
             color_discrete_sequence=px.colors.qualitative.Prism)
fig.show()


def gt_n(n):
    countries = full_grouped[full_grouped['Confirmed']>n]['Country/Region'].unique()
    temp = full_table[full_table['Country/Region'].isin(countries)]
    temp = temp.groupby(['Country/Region', 'Date'])['Confirmed'].sum().reset_index()
    temp = temp[temp['Confirmed']>n]
    # print(temp.head())

    min_date = temp.groupby('Country/Region')['Date'].min().reset_index()
    min_date.columns = ['Country/Region', 'Min Date']
    # print(min_date.head())

    from_nth_case = pd.merge(temp, min_date, on='Country/Region')
    from_nth_case['Date'] = pd.to_datetime(from_nth_case['Date'])
    from_nth_case['Min Date'] = pd.to_datetime(from_nth_case['Min Date'])
    from_nth_case['N days'] = (from_nth_case['Date'] - from_nth_case['Min Date']).dt.days
    # print(from_nth_case.head())

    fig = px.line(from_nth_case, x='N days', y='Confirmed', color='Country/Region', 
                  title='N days from '+str(n)+' case', height=600)
    fig.show()


gt_n(100000)


fig = px.scatter(country_wise.sort_values('Deaths', ascending=False).iloc[:20, :], 
                 x='Confirmed', y='Deaths', color='Country/Region', size='Confirmed', 
                 height=700, text='Country/Region', log_x=True, log_y=True, 
                 title='Deaths vs Confirmed (Scale is in log10)')
fig.update_traces(textposition='top center')
fig.update_layout(showlegend=False)
fig.update_layout(xaxis_rangeslider_visible=True)
fig.show()


def plot_treemap(col):
    fig = px.treemap(country_wise, path=["Country/Region"], values=col, height=700,
                 title=col, color_discrete_sequence = px.colors.qualitative.Dark2)
    fig.data[0].textinfo = 'label+text+value'
    fig.show()


plot_treemap('Confirmed')


plot_treemap('Deaths')


def plot_bubble(col, pal):
    temp = full_grouped[full_grouped[col]>0].sort_values('Country/Region', ascending=False)
    fig = px.scatter(temp, x='Date', y='Country/Region', size=col, color=col, height=3000,
                    color_continuous_scale=pal)
    fig.update_layout(yaxis = dict(dtick = 1))
    fig.update(layout_coloraxis_showscale=False)
    fig.show()


plot_bubble('New cases', 'Viridis')


plot_bubble('Active', 'Viridis')


temp = full_grouped[['Date', 'Country/Region', 'New cases']]
temp['New cases reported ?'] = temp['New cases']!=0
temp['New cases reported ?'] = temp['New cases reported ?'].astype(int)
# temp.head()


fig = go.Figure(data=go.Heatmap(
        z=temp['New cases reported ?'],
        x=temp['Date'],
        y=temp['Country/Region'],
        colorscale='Emrld',
        showlegend=False,
        text=temp['New cases reported ?']))

fig.update_layout(yaxis = dict(dtick = 1))
fig.update_layout(height=3000)
fig.show()


# HTML('''<div class="flourish-embed flourish-bar-chart-race" data-src="visualisation/1571387"><script src="https://public.flourish.studio/resources/embed.js"></script></div>''')


temp = full_table.groupby(['Country/Region', 'Date', ])['Confirmed', 'Deaths']
temp = temp.sum().diff().reset_index()

mask = temp['Country/Region'] != temp['Country/Region'].shift(1)

temp.loc[mask, 'Confirmed'] = np.nan
temp.loc[mask, 'Deaths'] = np.nan

countries = full_table[full_table['Confirmed']>100000]['Country/Region'].unique()

temp = temp[temp['Country/Region'].isin(countries)]


n_cols = 3
n_rows = math.ceil(len(countries)/n_cols)

fig = make_subplots(rows=n_rows, cols=n_cols, shared_xaxes=False, subplot_titles=countries)

for ind, country in enumerate(countries):
    row = int((ind/n_cols)+1)
    col = int((ind%n_cols)+1)
    fig.add_trace(go.Bar(x=temp['Date'], y=temp.loc[temp['Country/Region']==country, 'Confirmed'], name=country), row=row, col=col)
    
fig.update_layout(height=4000, title_text="No. of new cases in each Country")
fig.update_layout(showlegend=False)
fig.show()


usa_df = pd.read_csv('./Covid 19 data/usa_county_wise.csv')
usa_latest = usa_df[usa_df['Date'] == max(usa_df['Date'])]
usa_grouped = usa_latest.groupby('Province_State')['Confirmed', 'Deaths'].sum().reset_index()


us_code = {'Alabama': 'AL', 'Alaska': 'AK', 'American Samoa': 'AS', 'Arizona': 'AZ', 'Arkansas': 'AR', 
    'California': 'CA','Colorado': 'CO','Connecticut': 'CT','Delaware': 'DE', 'District of Columbia': 'DC', 
    'Florida': 'FL', 'Georgia': 'GA', 'Guam': 'GU', 'Hawaii': 'HI', 'Idaho': 'ID', 'Illinois': 'IL',
    'Indiana': 'IN','Iowa': 'IA','Kansas': 'KS','Kentucky': 'KY','Louisiana': 'LA','Maine': 'ME',
    'Maryland': 'MD','Massachusetts': 'MA','Michigan': 'MI','Minnesota': 'MN','Mississippi': 'MS',
    'Missouri': 'MO','Montana': 'MT','Nebraska': 'NE','Nevada': 'NV','New Hampshire': 'NH', 'New Jersey': 'NJ',
    'New Mexico': 'NM', 'New York': 'NY', 'North Carolina': 'NC', 'North Dakota': 'ND', 'Northern Mariana Islands':'MP',
    'Ohio': 'OH', 'Oklahoma': 'OK', 'Oregon': 'OR', 'Pennsylvania': 'PA', 'Puerto Rico': 'PR',
    'Rhode Island': 'RI', 'South Carolina': 'SC', 'South Dakota': 'SD', 'Tennessee': 'TN', 'Texas': 'TX',
    'Utah': 'UT', 'Vermont': 'VT', 'Virgin Islands': 'VI', 'Virginia': 'VA', 'Washington': 'WA',
    'West Virginia': 'WV', 'Wisconsin': 'WI', 'Wyoming': 'WY'}

usa_grouped['Code'] = usa_grouped['Province_State'].map(us_code)


fig = px.choropleth(usa_grouped, color='Confirmed', locations='Code', locationmode="USA-states", 
                    scope="usa", color_continuous_scale="RdGy", title='No. of cases in USA')
fig


with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

fig = px.choropleth(usa_latest, geojson=counties, locations='FIPS', color='Confirmed',
                           color_continuous_scale="Peach",
                           scope="usa",
                           labels={'Confirmed':'Confirmed'})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()


who = country_wise.groupby('WHO Region')['Confirmed', 'Deaths', 'Recovered', 'Active',
                                         'New cases', 'Confirmed last week'].sum().reset_index()
who['Fatality Rate'] = round((who['Deaths'] / who['Confirmed']) * 100, 2)
who['Recovery Rate'] = (who['Recovered'] / who['Confirmed']) * 100

who_g = full_grouped.groupby(['WHO Region', 'Date'])['Confirmed', 'Deaths', 'Recovered', 
                                                     'Active','New cases', 'New deaths'].sum().reset_index()


def plot_hbar(col, hover_data=[]):
    fig = px.bar(who.sort_values(col), 
                 x=col, y="WHO Region", color='WHO Region',  
                 text=col, orientation='h', width=700, hover_data=hover_data,
                 color_discrete_sequence = px.colors.qualitative.Dark2)
    fig.update_layout(title=col, xaxis_title="", yaxis_title="", 
                      yaxis_categoryorder = 'total ascending',
                      uniformtext_minsize=8, uniformtext_mode='hide')
    fig.show()


plot_hbar('Confirmed')


plot_hbar('Deaths')


plot_hbar('Fatality Rate')


temp = worldometer_data[worldometer_data['WHO Region']!=0]


fig = px.scatter(temp, x='TotalCases', y='TotalDeaths', color='WHO Region', 
                 height=700, hover_name='Country/Region', log_x=True, log_y=True, 
                 title='Confirmed vs Deaths',
                 color_discrete_sequence=px.colors.qualitative.Vivid)
fig.update_traces(textposition='top center')
# fig.update_layout(showlegend=False)
# fig.update_layout(xaxis_rangeslider_visible=True)
fig.show()


fig = px.scatter(temp, x='Population', y='TotalCases', color='WHO Region', 
                 height=700, hover_name='Country/Region', log_x=True, log_y=True, 
                 title='Population vs Confirmed',
                 color_discrete_sequence=px.colors.qualitative.Vivid)
fig.update_traces(textposition='top center')
# fig.update_layout(showlegend=False)
# fig.update_layout(xaxis_rangeslider_visible=True)
fig.show()


px.bar(who_g, x="Date", y="Confirmed", color='WHO Region', 
       height=600, title='Confirmed', 
       color_discrete_sequence=px.colors.qualitative.Vivid)


px.bar(who_g, x="Date", y="New cases", color='WHO Region', 
       height=600, title='New cases', 
       color_discrete_sequence=px.colors.qualitative.Vivid)


px.box(worldometer_data, x='WHO Region', y='TotalCases', color='WHO Region',
       title='Distribution of country wise no. of cases in different WHO Region')


full_grouped['Week No.'] = full_grouped['Date'].dt.strftime('%U')
week_wise = full_grouped.groupby('Week No.')['Confirmed', 'Deaths', 'Recovered', 'Active', 'New cases', 'New deaths', 'New recovered'].sum().reset_index()


def plot_weekwise(col, hue):
    fig = px.bar(week_wise, x="Week No.", y=col, width=700, color_discrete_sequence=[hue])
    fig.update_layout(title=col, xaxis_title="", yaxis_title="")
    fig.show()


plot_weekwise('Confirmed', '#000000')


plot_weekwise('Deaths', dth)


plot_weekwise('New cases', '#cd6684')


full_grouped['Month'] = pd.DatetimeIndex(full_grouped['Date']).month
month_wise = full_grouped.groupby('Month')['Confirmed', 'Deaths', 'Recovered', 'Active', 'New cases', 'New deaths', 'New recovered'].sum().reset_index()


def plot_monthwise(col, hue):
    fig = px.bar(month_wise, x="Month", y=col, width=700, color_discrete_sequence=[hue])
    fig.update_layout(title=col, xaxis_title="", yaxis_title="")
    fig.show()


plot_monthwise('Confirmed', '#000000')


plot_monthwise('Deaths', dth)


plot_monthwise('New cases', '#cd6684')


temp = country_wise[country_wise['Active']==0]
temp = temp.sort_values('Confirmed', ascending=False)
temp.reset_index(drop=True)


happiness_report = pd.read_csv('./World Happiness Report/2019.csv')
happiness_report = happiness_report[['Country or region', 'Healthy life expectancy']]

temp = country_wise.merge(happiness_report, left_on='Country/Region', right_on='Country or region')
px.scatter(temp, y='Deaths / 100 Cases', x='Healthy life expectancy', color='WHO Region', hover_data=['Country/Region'])


jupyter nbconvert --to mypackage.MyExporter covid-19-analysis-visualization-comparisons.ipynb

  Input In [109]
    jupyter nbconvert --to mypackage.MyExporter covid-19-analysis-visualization-comparisons.ipynb
            ^
SyntaxError: invalid syntax


import nbconvert

	Date	Country/Region	Confirmed_y	Deaths_y
0	2020-01-22	Afghanistan	555	17
1	2020-01-22	Albania	555	17
2	2020-01-22	Algeria	555	17
3	2020-01-22	Andorra	555	17
4	2020-01-22	Angola	555	17

	Country/Region	Confirmed	Deaths	Recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	Confirmed last week	WHO Region
0	San Marino	699	42	657	6.01	93.99	6.39	699	Europe
1	Brunei	141	3	138	2.13	97.87	2.17	141	Western Pacific
2	Grenada	23	0	23	0.00	100.00	0.00	23	Americas
3	Dominica	18	0	18	0.00	100.00	0.00	18	Americas
4	Holy See	12	0	12	0.00	100.00	0.00	12	Europe

Timeline¶

Ships that had passengers with COVID-19¶

Libraries¶

Install libraries¶

Load libraries¶

Color, theme, color palettes¶

Dataset¶

Latest¶

Maps¶

Cases over the time¶

Top 20 Countries¶

Date vs¶

Graph after 1M cases¶

Confirmed vs Deaths¶

Composition of Cases¶

Bubble Plot¶

Epidemic Span¶

Running Bar Chart¶

Country Wise¶

Comparison with similar epidemics¶

Analysis on similar pandemic/epidemic/outbreak¶

USA¶

WHO Region Wise¶

Weekly Statistics¶

Monthly statistics¶

Countries with no more active cases¶

Healthy life expectancy vs Deaths / 100 Cases¶